Beispiel #1
0
    def __init__(self,
                 hash_oov_words=False,
                 number_of_samples=10,
                 burn_in_sweeps=5):
        Inferencer.__init__(self, hash_oov_words)

        self._number_of_samples = number_of_samples
        self._burn_in_sweeps = burn_in_sweeps
Beispiel #2
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    def __init__(self,
                 hash_oov_words=False,
                 maximum_gamma_update_iteration=50,
                 minimum_mean_change_threshold=1e-3):
        Inferencer.__init__(self, hash_oov_words)

        self._maximum_gamma_update_iteration = maximum_gamma_update_iteration
        self._minimum_mean_change_threshold = minimum_mean_change_threshold
    def __init__(self,
                 hyper_parameter_optimize_interval=1,
                 symmetric_alpha_alpha=True,
                 symmetric_alpha_beta=True,
                 ):
        Inferencer.__init__(self, hyper_parameter_optimize_interval);

        self._symmetric_alpha_alpha = symmetric_alpha_alpha
        self._symmetric_alpha_beta = symmetric_alpha_beta
Beispiel #4
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 def __init__(self,
              hash_oov_words=False,
              maximum_gamma_update_iteration=50,
              minimum_mean_change_threshold=1e-3
              ):
     Inferencer.__init__(self, hash_oov_words);
     
     self._maximum_gamma_update_iteration = maximum_gamma_update_iteration;
     self._minimum_mean_change_threshold = minimum_mean_change_threshold;
Beispiel #5
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 def __init__(self,
              hyper_parameter_optimize_interval=1,
              
              #hyper_parameter_iteration=100,
              #hyper_parameter_decay_factor=0.9,
              #hyper_parameter_maximum_decay=10,
              #hyper_parameter_converge_threshold=1e-6,
              ):
     Inferencer.__init__(self, hyper_parameter_optimize_interval);
Beispiel #6
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 def __init__(self,
              hash_oov_words=False,
              number_of_samples=10,
              burn_in_sweeps=5
              ):
     Inferencer.__init__(self, hash_oov_words);
     
     self._number_of_samples = number_of_samples;
     self._burn_in_sweeps = burn_in_sweeps;
Beispiel #7
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    def __init__(
        self,
        hyper_parameter_optimize_interval=1,

        #hyper_parameter_iteration=100,
        #hyper_parameter_decay_factor=0.9,
        #hyper_parameter_maximum_decay=10,
        #hyper_parameter_converge_threshold=1e-6,
    ):
        Inferencer.__init__(self, hyper_parameter_optimize_interval)
Beispiel #8
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    def __init__(
        self,
        hyper_parameter_optimize_interval=10,
        symmetric_alpha_alpha=True,
        symmetric_alpha_beta=True,
    ):
        Inferencer.__init__(self, hyper_parameter_optimize_interval)

        self._symmetric_alpha_alpha = symmetric_alpha_alpha
        self._symmetric_alpha_beta = symmetric_alpha_beta
Beispiel #9
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    def __init__(self,
                 hyper_parameter_optimize_interval=10,
                 symmetric_alpha_alpha=True,
                 symmetric_alpha_beta=True,

                 #local_parameter_iteration=1,
                 ):
        Inferencer.__init__(self, hyper_parameter_optimize_interval);

        self._symmetric_alpha_alpha=symmetric_alpha_alpha
        self._symmetric_alpha_beta=symmetric_alpha_beta
    def __init__(self,
                 hyper_parameter_optimize_interval=1,
                 symmetric_alpha_alpha=True,
                 symmetric_alpha_beta=True,
                 #scipy_optimization_method="BFGS",
                 scipy_optimization_method="L-BFGS-B",
                 #scipy_optimization_method = "CG"
                 ):
        Inferencer.__init__(self, hyper_parameter_optimize_interval);

        self._symmetric_alpha_alpha = symmetric_alpha_alpha
        self._symmetric_alpha_beta = symmetric_alpha_beta
        
        self._scipy_optimization_method = scipy_optimization_method
Beispiel #11
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 def __init__(self,
              hyper_parameter_optimize_interval=1,
              ):
     '''
     update_hyper_parameter=True,
     alpha_update_decay_factor=0.9,
     alpha_maximum_decay=10,
     alpha_converge_threshold=0.000001,
     alpha_maximum_iteration=100,
     model_likelihood_threshold=0.00001,
     
     gamma_converge_threshold=0.000001,
     gamma_maximum_iteration=20
     '''
     
     Inferencer.__init__(self, hyper_parameter_optimize_interval);
Beispiel #12
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 def __init__(self,
              update_hyper_parameter=True,
              alpha_update_decay_factor=0.9,
              alpha_maximum_decay=10,
              alpha_converge_threshold=0.000001,
              alpha_maximum_iteration=100,
              model_likelihood_threshold=0.00001,
              number_of_samples=10,
              burn_in_samples=5
              ):
     Inferencer.__init__(self, update_hyper_parameter, alpha_update_decay_factor, alpha_maximum_decay, alpha_converge_threshold, alpha_maximum_iteration, model_likelihood_threshold);
     
     #self._alpha_update_decay_factor = alpha_update_decay_factor;
     #self._alpha_maximum_decay = alpha_maximum_decay;
     #self._alpha_converge_threshold = alpha_converge_threshold;
     #self._alpha_maximum_iteration = alpha_maximum_iteration;
     
     self._number_of_samples = number_of_samples;
     self._burn_in_samples = burn_in_samples;
Beispiel #13
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 def __init__(self,
              update_hyper_parameter=True,
              alpha_update_decay_factor=0.9,
              alpha_maximum_decay=10,
              alpha_converge_threshold=0.000001,
              alpha_maximum_iteration=100,
              model_likelihood_threshold=0.00001,
              gamma_converge_threshold=0.000001,
              gamma_maximum_iteration=20
              ):
     Inferencer.__init__(self, update_hyper_parameter, alpha_update_decay_factor, alpha_maximum_decay, alpha_converge_threshold, alpha_maximum_iteration, model_likelihood_threshold);
     
     #self._alpha_update_decay_factor = alpha_update_decay_factor;
     #self._alpha_maximum_decay = alpha_maximum_decay;
     #self._alpha_converge_threshold = alpha_converge_threshold;
     #self._alpha_maximum_iteration = alpha_maximum_iteration;
     
     self._gamma_maximum_iteration = gamma_maximum_iteration;
     self._gamma_converge_threshold = gamma_converge_threshold;